Overview

Dataset statistics

Number of variables21
Number of observations40494
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.5 MiB
Average record size in memory169.0 B

Variable types

Categorical10
Numeric10
Boolean1

Alerts

City Center (km) has unique valuesUnique
Metro Distance (km) has unique valuesUnique
Attraction Index has unique valuesUnique
Restraunt Index has unique valuesUnique
Bedrooms has 3728 (9.2%) zerosZeros

Reproduction

Analysis started2024-05-17 13:11:09.283467
Analysis finished2024-05-17 13:11:21.230164
Duration11.95 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

City
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size632.7 KiB
Rome
8978 
Paris
6224 
Lisbon
5677 
Athens
5264 
Budapest
3996 
Other values (4)
10355 

Length

Max length9
Median length8
Mean length5.9308046
Min length4

Characters and Unicode

Total characters240162
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAmsterdam
2nd rowAmsterdam
3rd rowAmsterdam
4th rowAmsterdam
5th rowAmsterdam

Common Values

ValueCountFrequency (%)
Rome 8978
22.2%
Paris 6224
15.4%
Lisbon 5677
14.0%
Athens 5264
13.0%
Budapest 3996
9.9%
Vienna 3511
 
8.7%
Barcelona 2731
 
6.7%
Berlin 2382
 
5.9%
Amsterdam 1731
 
4.3%

Length

2024-05-17T15:11:21.285177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:11:21.409204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rome 8978
22.2%
paris 6224
15.4%
lisbon 5677
14.0%
athens 5264
13.0%
budapest 3996
9.9%
vienna 3511
 
8.7%
barcelona 2731
 
6.7%
berlin 2382
 
5.9%
amsterdam 1731
 
4.3%

Most occurring characters

ValueCountFrequency (%)
e 28593
11.9%
n 23076
 
9.6%
s 22892
 
9.5%
a 20924
 
8.7%
i 17794
 
7.4%
o 17386
 
7.2%
r 13068
 
5.4%
m 12440
 
5.2%
t 10991
 
4.6%
B 9109
 
3.8%
Other values (12) 63889
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 240162
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 28593
11.9%
n 23076
 
9.6%
s 22892
 
9.5%
a 20924
 
8.7%
i 17794
 
7.4%
o 17386
 
7.2%
r 13068
 
5.4%
m 12440
 
5.2%
t 10991
 
4.6%
B 9109
 
3.8%
Other values (12) 63889
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 240162
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 28593
11.9%
n 23076
 
9.6%
s 22892
 
9.5%
a 20924
 
8.7%
i 17794
 
7.4%
o 17386
 
7.2%
r 13068
 
5.4%
m 12440
 
5.2%
t 10991
 
4.6%
B 9109
 
3.8%
Other values (12) 63889
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 240162
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 28593
11.9%
n 23076
 
9.6%
s 22892
 
9.5%
a 20924
 
8.7%
i 17794
 
7.4%
o 17386
 
7.2%
r 13068
 
5.4%
m 12440
 
5.2%
t 10991
 
4.6%
B 9109
 
3.8%
Other values (12) 63889
26.6%

Price
Real number (ℝ)

Distinct7456
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean237.44092
Minimum34.779339
Maximum799.70174
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size632.7 KiB
2024-05-17T15:11:21.542234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum34.779339
5-th percentile92.798725
Q1143.65055
median201.76279
Q3288.95815
95-th percentile522.63815
Maximum799.70174
Range764.9224
Interquartile range (IQR)145.3076

Descriptive statistics

Standard deviation134.16308
Coefficient of variation (CV)0.56503775
Kurtosis2.6014503
Mean237.44092
Median Absolute Deviation (MAD)67.473689
Skewness1.5523415
Sum9614932.6
Variance17999.733
MonotonicityNot monotonic
2024-05-17T15:11:21.662262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
184.4621607 188
 
0.5%
161.5505108 167
 
0.4%
126.7154513 162
 
0.4%
115.9984065 145
 
0.4%
138.9637476 133
 
0.3%
207.6076029 128
 
0.3%
104.2813957 128
 
0.3%
149.8608936 127
 
0.3%
103.8038015 124
 
0.3%
230.5192528 116
 
0.3%
Other values (7446) 39076
96.5%
ValueCountFrequency (%)
34.77933919 1
 
< 0.1%
37.12929454 1
 
< 0.1%
39.00925882 1
 
< 0.1%
40.1842365 1
 
< 0.1%
42.88425937 5
< 0.1%
44.1791606 1
 
< 0.1%
45.22766152 8
< 0.1%
46.05709209 3
 
< 0.1%
46.16502238 2
 
< 0.1%
46.39936259 3
 
< 0.1%
ValueCountFrequency (%)
799.7017429 3
 
< 0.1%
799.4687296 2
 
< 0.1%
799.2357163 1
 
< 0.1%
798.8657934 3
 
< 0.1%
798.752531 1
 
< 0.1%
798.6314531 12
< 0.1%
798.5366763 2
 
< 0.1%
797.6940923 1
 
< 0.1%
797.6535477 1
 
< 0.1%
797.3716097 4
 
< 0.1%

Day
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size632.7 KiB
Weekday
20284 
Weekend
20210 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters283458
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWeekday
2nd rowWeekday
3rd rowWeekday
4th rowWeekday
5th rowWeekday

Common Values

ValueCountFrequency (%)
Weekday 20284
50.1%
Weekend 20210
49.9%

Length

2024-05-17T15:11:21.769286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:11:21.864307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
weekday 20284
50.1%
weekend 20210
49.9%

Most occurring characters

ValueCountFrequency (%)
e 101198
35.7%
W 40494
14.3%
k 40494
14.3%
d 40494
14.3%
a 20284
 
7.2%
y 20284
 
7.2%
n 20210
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 283458
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 101198
35.7%
W 40494
14.3%
k 40494
14.3%
d 40494
14.3%
a 20284
 
7.2%
y 20284
 
7.2%
n 20210
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 283458
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 101198
35.7%
W 40494
14.3%
k 40494
14.3%
d 40494
14.3%
a 20284
 
7.2%
y 20284
 
7.2%
n 20210
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 283458
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 101198
35.7%
W 40494
14.3%
k 40494
14.3%
d 40494
14.3%
a 20284
 
7.2%
y 20284
 
7.2%
n 20210
 
7.1%

Room Type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size632.7 KiB
Entire home/apt
27455 
Private room
13039 

Length

Max length15
Median length15
Mean length14.034005
Min length12

Characters and Unicode

Total characters568293
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate room
2nd rowPrivate room
3rd rowPrivate room
4th rowPrivate room
5th rowPrivate room

Common Values

ValueCountFrequency (%)
Entire home/apt 27455
67.8%
Private room 13039
32.2%

Length

2024-05-17T15:11:21.950327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:11:22.055351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
entire 27455
33.9%
home/apt 27455
33.9%
private 13039
16.1%
room 13039
16.1%

Most occurring characters

ValueCountFrequency (%)
t 67949
12.0%
e 67949
12.0%
r 53533
9.4%
o 53533
9.4%
i 40494
 
7.1%
40494
 
7.1%
m 40494
 
7.1%
a 40494
 
7.1%
E 27455
 
4.8%
n 27455
 
4.8%
Other values (5) 108443
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 568293
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 67949
12.0%
e 67949
12.0%
r 53533
9.4%
o 53533
9.4%
i 40494
 
7.1%
40494
 
7.1%
m 40494
 
7.1%
a 40494
 
7.1%
E 27455
 
4.8%
n 27455
 
4.8%
Other values (5) 108443
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 568293
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 67949
12.0%
e 67949
12.0%
r 53533
9.4%
o 53533
9.4%
i 40494
 
7.1%
40494
 
7.1%
m 40494
 
7.1%
a 40494
 
7.1%
E 27455
 
4.8%
n 27455
 
4.8%
Other values (5) 108443
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 568293
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 67949
12.0%
e 67949
12.0%
r 53533
9.4%
o 53533
9.4%
i 40494
 
7.1%
40494
 
7.1%
m 40494
 
7.1%
a 40494
 
7.1%
E 27455
 
4.8%
n 27455
 
4.8%
Other values (5) 108443
19.1%

Person Capacity
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size632.7 KiB
2.0
17878 
4.0
11675 
3.0
5233 
6.0
3247 
5.0
2461 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters121482
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row2.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 17878
44.1%
4.0 11675
28.8%
3.0 5233
 
12.9%
6.0 3247
 
8.0%
5.0 2461
 
6.1%

Length

2024-05-17T15:11:22.137369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:11:22.242393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 17878
44.1%
4.0 11675
28.8%
3.0 5233
 
12.9%
6.0 3247
 
8.0%
5.0 2461
 
6.1%

Most occurring characters

ValueCountFrequency (%)
. 40494
33.3%
0 40494
33.3%
2 17878
14.7%
4 11675
 
9.6%
3 5233
 
4.3%
6 3247
 
2.7%
5 2461
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121482
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 40494
33.3%
0 40494
33.3%
2 17878
14.7%
4 11675
 
9.6%
3 5233
 
4.3%
6 3247
 
2.7%
5 2461
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121482
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 40494
33.3%
0 40494
33.3%
2 17878
14.7%
4 11675
 
9.6%
3 5233
 
4.3%
6 3247
 
2.7%
5 2461
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121482
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 40494
33.3%
0 40494
33.3%
2 17878
14.7%
4 11675
 
9.6%
3 5233
 
4.3%
6 3247
 
2.7%
5 2461
 
2.0%

Superhost
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size355.9 KiB
False
29080 
True
11414 
ValueCountFrequency (%)
False 29080
71.8%
True 11414
 
28.2%
2024-05-17T15:11:22.352417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Multiple Rooms
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size632.7 KiB
0
28444 
1
12050 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40494
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28444
70.2%
1 12050
29.8%

Length

2024-05-17T15:11:22.434436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:11:22.529458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 28444
70.2%
1 12050
29.8%

Most occurring characters

ValueCountFrequency (%)
0 28444
70.2%
1 12050
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40494
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 28444
70.2%
1 12050
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40494
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 28444
70.2%
1 12050
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40494
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 28444
70.2%
1 12050
29.8%

Business
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size632.7 KiB
0
26729 
1
13765 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40494
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 26729
66.0%
1 13765
34.0%

Length

2024-05-17T15:11:22.610476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:11:22.705497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 26729
66.0%
1 13765
34.0%

Most occurring characters

ValueCountFrequency (%)
0 26729
66.0%
1 13765
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40494
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 26729
66.0%
1 13765
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40494
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 26729
66.0%
1 13765
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40494
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 26729
66.0%
1 13765
34.0%

Cleanliness Rating
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4444115
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size632.7 KiB
2024-05-17T15:11:22.783515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q19
median10
Q310
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.88682922
Coefficient of variation (CV)0.093899892
Kurtosis14.731291
Mean9.4444115
Median Absolute Deviation (MAD)0
Skewness-2.9010028
Sum382442
Variance0.78646606
MonotonicityNot monotonic
2024-05-17T15:11:22.867534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
10 24350
60.1%
9 12067
29.8%
8 2922
 
7.2%
7 599
 
1.5%
6 334
 
0.8%
2 85
 
0.2%
4 77
 
0.2%
5 54
 
0.1%
3 6
 
< 0.1%
ValueCountFrequency (%)
2 85
 
0.2%
3 6
 
< 0.1%
4 77
 
0.2%
5 54
 
0.1%
6 334
 
0.8%
7 599
 
1.5%
8 2922
 
7.2%
9 12067
29.8%
10 24350
60.1%
ValueCountFrequency (%)
10 24350
60.1%
9 12067
29.8%
8 2922
 
7.2%
7 599
 
1.5%
6 334
 
0.8%
5 54
 
0.1%
4 77
 
0.2%
3 6
 
< 0.1%
2 85
 
0.2%

Guest Satisfaction
Real number (ℝ)

Distinct51
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.096755
Minimum20
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size632.7 KiB
2024-05-17T15:11:22.978559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile80
Q190
median95
Q398
95-th percentile100
Maximum100
Range80
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.1288534
Coefficient of variation (CV)0.087316185
Kurtosis19.135205
Mean93.096755
Median Absolute Deviation (MAD)4
Skewness-3.2651733
Sum3769860
Variance66.078258
MonotonicityNot monotonic
2024-05-17T15:11:23.095587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 7989
19.7%
98 3200
 
7.9%
97 3081
 
7.6%
96 2975
 
7.3%
93 2730
 
6.7%
95 2723
 
6.7%
94 2252
 
5.6%
90 2032
 
5.0%
99 1913
 
4.7%
92 1676
 
4.1%
Other values (41) 9923
24.5%
ValueCountFrequency (%)
20 89
0.2%
30 2
 
< 0.1%
40 62
0.2%
44 2
 
< 0.1%
47 11
 
< 0.1%
50 28
 
0.1%
53 10
 
< 0.1%
54 1
 
< 0.1%
55 2
 
< 0.1%
56 5
 
< 0.1%
ValueCountFrequency (%)
100 7989
19.7%
99 1913
 
4.7%
98 3200
7.9%
97 3081
 
7.6%
96 2975
 
7.3%
95 2723
 
6.7%
94 2252
 
5.6%
93 2730
 
6.7%
92 1676
 
4.1%
91 1470
 
3.6%

Bedrooms
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1493061
Minimum0
Maximum10
Zeros3728
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size632.7 KiB
2024-05-17T15:11:23.197609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.62317415
Coefficient of variation (CV)0.54221775
Kurtosis9.5816708
Mean1.1493061
Median Absolute Deviation (MAD)0
Skewness1.3239539
Sum46540
Variance0.38834602
MonotonicityNot monotonic
2024-05-17T15:11:23.279627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 28233
69.7%
2 7418
 
18.3%
0 3728
 
9.2%
3 1055
 
2.6%
4 44
 
0.1%
9 10
 
< 0.1%
5 4
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
0 3728
 
9.2%
1 28233
69.7%
2 7418
 
18.3%
3 1055
 
2.6%
4 44
 
0.1%
5 4
 
< 0.1%
9 10
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
10 2
 
< 0.1%
9 10
 
< 0.1%
5 4
 
< 0.1%
4 44
 
0.1%
3 1055
 
2.6%
2 7418
 
18.3%
1 28233
69.7%
0 3728
 
9.2%

City Center (km)
Real number (ℝ)

UNIQUE 

Distinct40494
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6850075
Minimum0.034660637
Maximum25.284557
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size632.7 KiB
2024-05-17T15:11:23.382650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.034660637
5-th percentile0.47792728
Q11.2795995
median2.2609707
Q33.5861015
95-th percentile6.2507304
Maximum25.284557
Range25.249896
Interquartile range (IQR)2.306502

Descriptive statistics

Standard deviation2.001226
Coefficient of variation (CV)0.74533347
Kurtosis9.672839
Mean2.6850075
Median Absolute Deviation (MAD)1.0961254
Skewness2.195764
Sum108726.69
Variance4.0049053
MonotonicityNot monotonic
2024-05-17T15:11:23.499677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.022963798 1
 
< 0.1%
4.226246551 1
 
< 0.1%
4.571697405 1
 
< 0.1%
4.480473645 1
 
< 0.1%
3.805042098 1
 
< 0.1%
3.756639837 1
 
< 0.1%
3.941680518 1
 
< 0.1%
5.016842116 1
 
< 0.1%
3.343744568 1
 
< 0.1%
3.52477265 1
 
< 0.1%
Other values (40484) 40484
> 99.9%
ValueCountFrequency (%)
0.03466063682 1
< 0.1%
0.03981359742 1
< 0.1%
0.04278870764 1
< 0.1%
0.04331472743 1
< 0.1%
0.04333710768 1
< 0.1%
0.05129393071 1
< 0.1%
0.0522373372 1
< 0.1%
0.05223932064 1
< 0.1%
0.05225782952 1
< 0.1%
0.05226807216 1
< 0.1%
ValueCountFrequency (%)
25.28455675 1
< 0.1%
22.61745814 1
< 0.1%
22.61745145 1
< 0.1%
22.59511526 1
< 0.1%
21.29517392 1
< 0.1%
21.29515096 1
< 0.1%
20.89510207 1
< 0.1%
20.89509463 1
< 0.1%
20.49567803 1
< 0.1%
20.49565772 1
< 0.1%

Metro Distance (km)
Real number (ℝ)

UNIQUE 

Distinct40494
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60724429
Minimum0.002301068
Maximum14.273577
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size632.7 KiB
2024-05-17T15:11:23.610702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.002301068
5-th percentile0.10622618
Q10.23821901
median0.39415911
Q30.68192966
95-th percentile1.7764546
Maximum14.273577
Range14.271276
Interquartile range (IQR)0.44371065

Descriptive statistics

Standard deviation0.71024106
Coefficient of variation (CV)1.1696134
Kurtosis35.889739
Mean0.60724429
Median Absolute Deviation (MAD)0.18919503
Skewness4.5499438
Sum24589.75
Variance0.50444236
MonotonicityNot monotonic
2024-05-17T15:11:23.720726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.539380003 1
 
< 0.1%
0.4673339809 1
 
< 0.1%
0.5239109255 1
 
< 0.1%
0.2872773788 1
 
< 0.1%
0.3430603066 1
 
< 0.1%
0.3573411382 1
 
< 0.1%
0.3970745879 1
 
< 0.1%
0.5647181525 1
 
< 0.1%
0.06130210104 1
 
< 0.1%
0.03403870962 1
 
< 0.1%
Other values (40484) 40484
> 99.9%
ValueCountFrequency (%)
0.002301068012 1
< 0.1%
0.003220007615 1
< 0.1%
0.003935058041 1
< 0.1%
0.00394375911 1
< 0.1%
0.004750441048 1
< 0.1%
0.004762350151 1
< 0.1%
0.006157628218 1
< 0.1%
0.006170744841 1
< 0.1%
0.006388847148 1
< 0.1%
0.006405009016 1
< 0.1%
ValueCountFrequency (%)
14.27357693 1
< 0.1%
13.31411503 1
< 0.1%
13.31410827 1
< 0.1%
13.0699635 1
< 0.1%
11.68773401 1
< 0.1%
9.598773284 1
< 0.1%
9.573733182 1
< 0.1%
8.979488226 1
< 0.1%
8.918049525 1
< 0.1%
8.918036013 1
< 0.1%

Attraction Index
Real number (ℝ)

UNIQUE 

Distinct40494
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.51583
Minimum15.152201
Maximum4513.5635
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size632.7 KiB
2024-05-17T15:11:23.844754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum15.152201
5-th percentile61.346107
Q1123.33187
median226.38367
Q3388.5517
95-th percentile746.5284
Maximum4513.5635
Range4498.4113
Interquartile range (IQR)265.21983

Descriptive statistics

Standard deviation235.38341
Coefficient of variation (CV)0.80744639
Kurtosis23.850178
Mean291.51583
Median Absolute Deviation (MAD)116.94645
Skewness2.8396698
Sum11804642
Variance55405.347
MonotonicityNot monotonic
2024-05-17T15:11:23.951779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.69037927 1
 
< 0.1%
235.8170018 1
 
< 0.1%
245.4984258 1
 
< 0.1%
262.8234054 1
 
< 0.1%
253.4821015 1
 
< 0.1%
283.3302822 1
 
< 0.1%
226.6700001 1
 
< 0.1%
228.6905741 1
 
< 0.1%
350.0160904 1
 
< 0.1%
288.6661589 1
 
< 0.1%
Other values (40484) 40484
> 99.9%
ValueCountFrequency (%)
15.15220147 1
< 0.1%
15.53291806 1
< 0.1%
16.58197413 1
< 0.1%
16.60073055 1
< 0.1%
16.60073515 1
< 0.1%
19.01913316 1
< 0.1%
19.01914767 1
< 0.1%
19.1378197 1
< 0.1%
19.13782442 1
< 0.1%
19.21394433 1
< 0.1%
ValueCountFrequency (%)
4513.563486 1
< 0.1%
4512.59517 1
< 0.1%
4512.345962 1
< 0.1%
4510.73735 1
< 0.1%
4510.436033 1
< 0.1%
4509.914049 1
< 0.1%
4022.618126 1
< 0.1%
3031.840298 1
< 0.1%
3028.991664 1
< 0.1%
2934.133441 1
< 0.1%
Distinct40477
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.513351
Minimum0.92630092
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size632.7 KiB
2024-05-17T15:11:24.270851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.92630092
5-th percentile2.7628132
Q15.4316636
median9.820346
Q315.187239
95-th percentile26.398209
Maximum100
Range99.073699
Interquartile range (IQR)9.7555752

Descriptive statistics

Standard deviation8.206475
Coefficient of variation (CV)0.71277903
Kurtosis12.357084
Mean11.513351
Median Absolute Deviation (MAD)4.7218661
Skewness2.2757912
Sum466221.63
Variance67.346232
MonotonicityNot monotonic
2024-05-17T15:11:24.385876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 18
 
< 0.1%
4.166707868 1
 
< 0.1%
14.60661287 1
 
< 0.1%
13.77695554 1
 
< 0.1%
11.02184522 1
 
< 0.1%
11.12009577 1
 
< 0.1%
17.01955781 1
 
< 0.1%
11.46661884 1
 
< 0.1%
14.03641294 1
 
< 0.1%
14.24576085 1
 
< 0.1%
Other values (40467) 40467
99.9%
ValueCountFrequency (%)
0.9263009179 1
< 0.1%
1.040227906 1
< 0.1%
1.040956301 1
< 0.1%
1.134200311 1
< 0.1%
1.135137404 1
< 0.1%
1.141278148 1
< 0.1%
1.142220498 1
< 0.1%
1.145817817 1
< 0.1%
1.147309596 1
< 0.1%
1.148114127 1
< 0.1%
ValueCountFrequency (%)
100 18
< 0.1%
99.9944775 1
 
< 0.1%
99.95215309 1
 
< 0.1%
99.93738572 1
 
< 0.1%
99.91914511 1
 
< 0.1%
99.27831239 1
 
< 0.1%
89.1228879 1
 
< 0.1%
83.4027044 1
 
< 0.1%
81.57382236 1
 
< 0.1%
81.47248213 1
 
< 0.1%

Restraunt Index
Real number (ℝ)

UNIQUE 

Distinct40494
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean625.43695
Minimum19.576924
Maximum6696.1568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size632.7 KiB
2024-05-17T15:11:24.499903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum19.576924
5-th percentile86.05981
Q1207.8776
median517.66171
Q3857.18616
95-th percentile1666.866
Maximum6696.1568
Range6676.5798
Interquartile range (IQR)649.30856

Descriptive statistics

Standard deviation523.11144
Coefficient of variation (CV)0.83639357
Kurtosis3.7046752
Mean625.43695
Median Absolute Deviation (MAD)318.12572
Skewness1.5560919
Sum25326444
Variance273645.58
MonotonicityNot monotonic
2024-05-17T15:11:24.610927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98.25389587 1
 
< 0.1%
514.3705213 1
 
< 0.1%
514.3139596 1
 
< 0.1%
543.9049903 1
 
< 0.1%
579.1593637 1
 
< 0.1%
603.7661164 1
 
< 0.1%
494.9824085 1
 
< 0.1%
471.7360899 1
 
< 0.1%
704.7632682 1
 
< 0.1%
659.1208236 1
 
< 0.1%
Other values (40484) 40484
> 99.9%
ValueCountFrequency (%)
19.5769238 1
< 0.1%
21.45579724 1
< 0.1%
21.45580338 1
< 0.1%
21.49691927 1
< 0.1%
25.02270288 1
< 0.1%
26.50937148 1
< 0.1%
26.72904038 1
< 0.1%
27.90161264 1
< 0.1%
27.90167346 1
< 0.1%
27.93416764 1
< 0.1%
ValueCountFrequency (%)
6696.156772 1
< 0.1%
4592.883342 1
< 0.1%
4591.339847 1
< 0.1%
4590.349641 1
< 0.1%
4590.306687 1
< 0.1%
4589.772131 1
< 0.1%
4589.32312 1
< 0.1%
4552.357526 1
< 0.1%
4542.75415 1
< 0.1%
4515.190626 1
< 0.1%
Distinct40478
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.214374
Minimum0.59275692
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size632.7 KiB
2024-05-17T15:11:24.732955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.59275692
5-th percentile2.4256216
Q110.872253
median21.438923
Q336.273249
95-th percentile61.125566
Maximum100
Range99.407243
Interquartile range (IQR)25.400996

Descriptive statistics

Standard deviation18.326631
Coefficient of variation (CV)0.7268327
Kurtosis0.32995632
Mean25.214374
Median Absolute Deviation (MAD)12.478404
Skewness0.8619155
Sum1021030.8
Variance335.86542
MonotonicityNot monotonic
2024-05-17T15:11:24.846981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 17
 
< 0.1%
6.846472824 1
 
< 0.1%
29.67129305 1
 
< 0.1%
22.83469266 1
 
< 0.1%
21.76228578 1
 
< 0.1%
32.51237287 1
 
< 0.1%
23.72911151 1
 
< 0.1%
30.40678047 1
 
< 0.1%
26.60065723 1
 
< 0.1%
29.75738238 1
 
< 0.1%
Other values (40468) 40468
99.9%
ValueCountFrequency (%)
0.5927569191 1
< 0.1%
0.6407212807 1
< 0.1%
0.6460305932 1
< 0.1%
0.6549731588 1
< 0.1%
0.6608714797 1
< 0.1%
0.6659262496 1
< 0.1%
0.6670097117 1
< 0.1%
0.6677877225 1
< 0.1%
0.6743712124 1
< 0.1%
0.6746584383 1
< 0.1%
ValueCountFrequency (%)
100 17
< 0.1%
99.96639378 1
 
< 0.1%
99.94483419 1
 
< 0.1%
99.94389898 1
 
< 0.1%
99.92248394 1
 
< 0.1%
99.19076175 1
 
< 0.1%
98.48673907 1
 
< 0.1%
98.30841086 1
 
< 0.1%
98.11497172 1
 
< 0.1%
98.05204 1
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size632.7 KiB
10
24350 
9
12067 
1-8
4077 

Length

Max length3
Median length2
Mean length1.8026868
Min length1

Characters and Unicode

Total characters72998
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row1-8
3rd row9
4th row9
5th row10

Common Values

ValueCountFrequency (%)
10 24350
60.1%
9 12067
29.8%
1-8 4077
 
10.1%

Length

2024-05-17T15:11:24.955005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:11:25.067030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
10 24350
60.1%
9 12067
29.8%
1-8 4077
 
10.1%

Most occurring characters

ValueCountFrequency (%)
1 28427
38.9%
0 24350
33.4%
9 12067
16.5%
- 4077
 
5.6%
8 4077
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72998
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 28427
38.9%
0 24350
33.4%
9 12067
16.5%
- 4077
 
5.6%
8 4077
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72998
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 28427
38.9%
0 24350
33.4%
9 12067
16.5%
- 4077
 
5.6%
8 4077
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72998
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 28427
38.9%
0 24350
33.4%
9 12067
16.5%
- 4077
 
5.6%
8 4077
 
5.6%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size632.7 KiB
91-95
10851 
98-100
9902 
95-98
9256 
81-90
7498 
1-80
2987 

Length

Max length6
Median length5
Mean length5.170766
Min length4

Characters and Unicode

Total characters209385
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row91-95
2nd row81-90
3rd row81-90
4th row81-90
5th row95-98

Common Values

ValueCountFrequency (%)
91-95 10851
26.8%
98-100 9902
24.5%
95-98 9256
22.9%
81-90 7498
18.5%
1-80 2987
 
7.4%

Length

2024-05-17T15:11:25.164052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:11:25.280079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
91-95 10851
26.8%
98-100 9902
24.5%
95-98 9256
22.9%
81-90 7498
18.5%
1-80 2987
 
7.4%

Most occurring characters

ValueCountFrequency (%)
9 57614
27.5%
- 40494
19.3%
1 31238
14.9%
0 30289
14.5%
8 29643
14.2%
5 20107
 
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 209385
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 57614
27.5%
- 40494
19.3%
1 31238
14.9%
0 30289
14.5%
8 29643
14.2%
5 20107
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 209385
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 57614
27.5%
- 40494
19.3%
1 31238
14.9%
0 30289
14.5%
8 29643
14.2%
5 20107
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 209385
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 57614
27.5%
- 40494
19.3%
1 31238
14.9%
0 30289
14.5%
8 29643
14.2%
5 20107
 
9.6%

Bedrooms Cat
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size632.7 KiB
1
28233 
2
7418 
0
3728 
3+
 
1115

Length

Max length2
Median length1
Mean length1.0275349
Min length1

Characters and Unicode

Total characters41609
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 28233
69.7%
2 7418
 
18.3%
0 3728
 
9.2%
3+ 1115
 
2.8%

Length

2024-05-17T15:11:25.380101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:11:25.483124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 28233
69.7%
2 7418
 
18.3%
0 3728
 
9.2%
3 1115
 
2.8%

Most occurring characters

ValueCountFrequency (%)
1 28233
67.9%
2 7418
 
17.8%
0 3728
 
9.0%
3 1115
 
2.7%
+ 1115
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41609
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 28233
67.9%
2 7418
 
17.8%
0 3728
 
9.0%
3 1115
 
2.7%
+ 1115
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41609
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 28233
67.9%
2 7418
 
17.8%
0 3728
 
9.0%
3 1115
 
2.7%
+ 1115
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41609
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 28233
67.9%
2 7418
 
17.8%
0 3728
 
9.0%
3 1115
 
2.7%
+ 1115
 
2.7%

Country
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size632.7 KiB
Italy
8978 
France
6224 
Portugal
5677 
Greece
5264 
Hungary
3996 
Other values (4)
10355 

Length

Max length11
Median length8
Mean length6.4491777
Min length5

Characters and Unicode

Total characters261153
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNetherlands
2nd rowNetherlands
3rd rowNetherlands
4th rowNetherlands
5th rowNetherlands

Common Values

ValueCountFrequency (%)
Italy 8978
22.2%
France 6224
15.4%
Portugal 5677
14.0%
Greece 5264
13.0%
Hungary 3996
9.9%
Austria 3511
 
8.7%
Spain 2731
 
6.7%
Germany 2382
 
5.9%
Netherlands 1731
 
4.3%

Length

2024-05-17T15:11:25.579146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T15:11:25.702174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
italy 8978
22.2%
france 6224
15.4%
portugal 5677
14.0%
greece 5264
13.0%
hungary 3996
9.9%
austria 3511
 
8.7%
spain 2731
 
6.7%
germany 2382
 
5.9%
netherlands 1731
 
4.3%

Most occurring characters

ValueCountFrequency (%)
a 35230
13.5%
r 28785
11.0%
e 27860
10.7%
t 19897
 
7.6%
n 17064
 
6.5%
l 16386
 
6.3%
y 15356
 
5.9%
u 13184
 
5.0%
c 11488
 
4.4%
g 9673
 
3.7%
Other values (15) 66230
25.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 261153
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 35230
13.5%
r 28785
11.0%
e 27860
10.7%
t 19897
 
7.6%
n 17064
 
6.5%
l 16386
 
6.3%
y 15356
 
5.9%
u 13184
 
5.0%
c 11488
 
4.4%
g 9673
 
3.7%
Other values (15) 66230
25.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 261153
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 35230
13.5%
r 28785
11.0%
e 27860
10.7%
t 19897
 
7.6%
n 17064
 
6.5%
l 16386
 
6.3%
y 15356
 
5.9%
u 13184
 
5.0%
c 11488
 
4.4%
g 9673
 
3.7%
Other values (15) 66230
25.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 261153
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 35230
13.5%
r 28785
11.0%
e 27860
10.7%
t 19897
 
7.6%
n 17064
 
6.5%
l 16386
 
6.3%
y 15356
 
5.9%
u 13184
 
5.0%
c 11488
 
4.4%
g 9673
 
3.7%
Other values (15) 66230
25.4%

Interactions

2024-05-17T15:11:19.665811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:10.369712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:11.372938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:12.513196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:13.534426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:14.478640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:15.587890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:16.582114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:17.527328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:18.489546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:19.769835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:10.473735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:11.477962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:12.619220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:13.634449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:14.578662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:15.692914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:16.682137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:17.629351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:18.595569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:19.874858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:10.579759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:11.717016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:12.727245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:13.734472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:14.680685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:15.798938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:16.785161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:17.731374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:18.700593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:19.978882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:10.686784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:11.824040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:12.833268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:13.836495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:14.782708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:15.905962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:16.887184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:17.835398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:18.809618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:20.071903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:10.782806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:11.920062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:12.933291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:13.924515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:14.872729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:15.998983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:16.975203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:17.925418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:18.904639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:20.171925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:10.879827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:12.016083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:13.033313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:14.014535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:14.962749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:16.094004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:17.066224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:18.018439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:19.001661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:20.272948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:10.981850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:12.121108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:13.138337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:14.111557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:15.060771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:16.194027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:17.162246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:18.117461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:19.104684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:20.364969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:11.075871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:12.214128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:13.232359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:14.198576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:15.303826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:16.286048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:17.249265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:18.207483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:19.196705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:20.459990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:11.171893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:12.311150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:13.331380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:14.289597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:15.396847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:16.383070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:17.339286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:18.298502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:19.294139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:20.560013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:11.275917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:12.415174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:13.436404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:14.386619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:15.494869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:16.485093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:17.437308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:18.398525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-17T15:11:19.566789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Missing values

2024-05-17T15:11:20.719049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-17T15:11:21.037121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CityPriceDayRoom TypePerson CapacitySuperhostMultiple RoomsBusinessCleanliness RatingGuest SatisfactionBedroomsCity Center (km)Metro Distance (km)Attraction IndexNormalised Attraction IndexRestraunt IndexNormalised Restraunt IndexCleanliness Rating CatGuest Satisfaction CatBedrooms CatCountry
0Amsterdam194.033698WeekdayPrivate room2.0False1010.093.015.0229642.53938078.6903794.16670898.2538966.8464731091-951Netherlands
1Amsterdam344.245776WeekdayPrivate room4.0False008.085.010.4883890.239404631.17637833.421209837.28075758.3429281-881-901Netherlands
2Amsterdam264.101422WeekdayPrivate room2.0False019.087.015.7483123.65162175.2758773.98590895.3869556.646700981-901Netherlands
3Amsterdam433.529398WeekdayPrivate room4.0False019.090.020.3848620.439876493.27253426.119108875.03309860.973565981-902Netherlands
4Amsterdam485.552926WeekdayPrivate room2.0True0010.098.010.5447380.318693552.83032429.272733815.30574056.8116771095-981Netherlands
5Amsterdam552.808567WeekdayPrivate room3.0False008.0100.022.1314201.904668174.7889579.255191225.20166215.6923761-898-1002Netherlands
6Amsterdam215.124317WeekdayPrivate room2.0False0010.094.011.8810920.729747200.16765210.599010242.76552416.9162511091-951Netherlands
9Amsterdam276.521454WeekdayPrivate room2.0False1010.088.013.1423610.924404206.25286210.921226238.29125816.6044781081-901Netherlands
11Amsterdam319.640053WeekdayPrivate room2.0True1010.097.012.1827071.590381191.50133910.140123229.29740115.9777731095-981Netherlands
12Amsterdam675.602840WeekdayEntire home/apt4.0False008.087.012.9330460.628073214.92334211.380334269.62490418.7878511-881-901Netherlands
CityPriceDayRoom TypePerson CapacitySuperhostMultiple RoomsBusinessCleanliness RatingGuest SatisfactionBedroomsCity Center (km)Metro Distance (km)Attraction IndexNormalised Attraction IndexRestraunt IndexNormalised Restraunt IndexCleanliness Rating CatGuest Satisfaction CatBedrooms CatCountry
41704Vienna463.501858WeekendEntire home/apt5.0False1010.090.021.0218780.285141176.75490012.658020283.4264596.8503081081-902Austria
41705Vienna727.391721WeekendEntire home/apt6.0False0110.096.030.5685620.230806209.89871915.031561411.5536339.9470931095-983+Austria
41706Vienna718.275951WeekendEntire home/apt6.0False0110.095.030.5658540.136006212.07761915.187600420.03013810.1519661091-953+Austria
41707Vienna115.933899WeekendPrivate room4.0False109.094.013.0419320.308192109.7513877.859670208.5178875.039797991-951Austria
41708Vienna750.765491WeekendEntire home/apt6.0False0110.096.030.3788040.203138257.49481718.440080548.97329613.2684731095-983+Austria
41709Vienna715.938574WeekendEntire home/apt6.0False0110.0100.030.5301810.135447219.40247815.712158438.75687410.6045841098-1003+Austria
41710Vienna304.793960WeekendEntire home/apt2.0False008.086.010.8102050.100839204.97012114.678608342.1828138.2704271-881-901Austria
41711Vienna637.168969WeekendEntire home/apt2.0False0010.093.010.9940510.202539169.07340212.107921282.2964246.8229961091-951Austria
41712Vienna301.054157WeekendPrivate room2.0False0010.087.013.0441000.287435109.2365747.822803158.5633983.8324161081-901Austria
41713Vienna133.230489WeekendPrivate room4.0True1010.093.011.2639320.480903150.45038110.774264225.2472935.4441401091-951Austria